# Example of KNN implemented from Scratch in Python

import csv
import random
import math
import operator

def loadDataset(filename, split, trainingSet=[], testSet=[]):
	with open(filename, 'rb') as csvfile:
		lines = csv.reader(csvfile)
		dataset = list(lines)
		for x in range(len(dataset)-1):
			for y in range(4):
				dataset[x][y] = float(dataset[x][y])
			if random.random()<split:
				trainingSet.append(dataset[x])
			else:
				testSet.append(dataset[x])

def euclideanDistance(instance1, instance2, length):
	distance = 0
	for x in range(length):
		distance += pow((instance1[x]-instance2[x]), 2)
	return math.sqrt(distance)

def getNeighbors(trainingSet, testInstance, k):
	distances = []
	length = len(testInstance)-1
	for x in range(len(trainingSet)):
		dist = euclideanDistance(testInstance, trainingSet[x], length)
		distances.append((trainingSet[x], dist))
	distances.sort(key=operator.itemgetter(1))
	neighbors = []
	for x in range(k):
		neighbors.append(distances[x][0])
	return neighbors

def getResponse(neighbors):
	classVotes = {}
	for x in range(len(neighbors)):
		response = neighbors[x][-1]
		if response in classVotes:
			classVotes[response] += 1
		else:
			classVotes[response] = 1
		sortedVotes = sorted(classVotes.iteritems(), key=operator.itemgetter(1), reverse=True)
		return sortedVotes[0][0]

def getAccuracy(testSet, predictions):
	correct = 0
	for x in range(len(testSet)):
		if testSet[x][-1] == predictions[x]:
			correct += 1
	return (correct/float(len(testSet))) * 100.0

def main():
	# prepare data
	trainingSet = []
	testSet = []
	split = 0.67
	loadDataset(r'datasets/iris.data.txt', split, trainingSet, testSet)
	print testSet
	print 'Train set:'+repr(len(trainingSet))
	print 'Test set:'+repr(len(testSet))
	# generate predictions
	predictions = []
	k = 3
	for x in range(len(testSet)):
		neighbors = getNeighbors(trainingSet, testSet[x], k)
		result = getResponse(neighbors)
		predictions.append(result)
		print "> predicted="+repr(result)+", actual="+repr(testSet[x][-1])
	accuracy = getAccuracy(testSet, predictions)
	print 'Accuracy'+repr(accuracy)+'%'

main()